Lodging Recommendations Using the SparkML Engine ALS and Surprise SVD

 (*)Sageri Fikri Ramadhan Mail (Telkom University, Bandung, Indonesia)
 Z K Abdurahman Baizal (Telkom University, Bandung, Indonesia)
 Rita Rismala (Telkom University, Bandung, Indonesia)

(*) Corresponding Author

Submitted: June 4, 2020; Published: October 20, 2020

DOI: http://dx.doi.org/10.30865/mib.v%25vi%25i.2257


Recommendation system is a process or tool used to provide predictions for users to choose something based on an existing domain. This system has become a primary need for today's modern digital industry such as in the entertainment, shopping, and service sectors. In this research, we focus on how to develop a recommendation system for accommodation services. We use the Alternating Least Square and Singular Value Decomposition methods to predict and recommend lodging to users


Recommendation, Lodging, ALS, SVD, Rating, NLTK

Full Text:


Article Metrics

Abstract View: 70 times | PDF View: 22 times


R. Prosser, “Tourism,” Encycl. Appl. Ethics, pp. 386–406, 2012, doi: 10.1016/B978-0-12-373932-2.00072-7.

UNWTO, “International Tourism Highlights,” UNWTO Tourism Highlights: 2019 Edition, 2019. .

R. Sharpley, Tourism, Tourists and Society. 2018.

N. Evans and N. Evans, “Airbnb,” in Strategic Management for Tourism, Hospitality and Events, 2019.

N. Deshai, B. V. D. S. Sekhar, and S. Venkataramana, “Mllib: machine learning in apache spark,” Int. J. Recent Technol. Eng., 2019.

I. S. Wahyudi, “Big data analytic untuk pembuatan rekomendasi koleksi film personal menggunakan Mlib. Apache Spark,” Berk. Ilmu Perpust. dan Inf., vol. 14, no. 1, p. 11, 2018, doi: 10.22146/bip.32208.

N. Hug, “Surprise, a Python library for recommender systems,” URL http//surpriselib. com, 2017.

R. Mhetre and D. P. G, “Movie Recommendation Engine using Collaborative Filtering with Alternative Least Square and Singular Value Decomposition Algorithms,” IJARCCE, 2019, doi: 10.17148/ijarcce.2019.8216.

L. Yanxiang, G. Deke, C. Fei, and C. Honghui, “User-based clustering with top-N recommendation on Cold-Start problem,” in Proceedings of the 2013 3rd International Conference on Intelligent System Design and Engineering Applications, ISDEA 2013, 2013, doi: 10.1109/ISDEA.2012.381.

G. Takács and D. Tikk, “Alternating least squares for personalized ranking,” in RecSys’12 - Proceedings of the 6th ACM Conference on Recommender Systems, 2012, doi: 10.1145/2365952.2365972.

X. Meng et al., “MLlib: Machine learning in Apache Spark,” J. Mach. Learn. Res., 2016.

H. R. M. , S. D. . Harish Rao M , Shashikumar D.R, “Automatic Product Review Sentiment Analysis Using Vader and Feature Visulaization,” Int. J. Comput. Sci. Eng. Inf. Technol. Res., 2017, doi: 10.24247/ijcseitraug20178.

C. J. Hutto and E. Gilbert, “VADER: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the 8th International Conference on Weblogs and Social Media, ICWSM 2014, 2014.

E. V. V. Cervantes, L. V. C. Quispe, and J. E. O. Luna, “Performance of alternating least squares in a distributed approach using GraphLab and MapReduce,” in CEUR Workshop Proceedings, 2015.

T. Chai and R. R. Draxler, “Root mean square error (RMSE) or mean absolute error (MAE)? -Arguments against avoiding RMSE in the literature,” Geosci. Model Dev., 2014, doi: 10.5194/gmd-7-1247-2014.

E. Hazan, A. Klivans, and Y. Yuan, “Hyperparameter optimization: A spectral approach,” in 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings, 2018.

J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., 2012.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Lodging Recommendations Using the SparkML Engine ALS and Surprise SVD


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email : mib.stmikbd@gmail.com

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.